CN113806453A - Automatic confirmation method, device, terminal and medium for scheduling operation ticket comprehensive order - Google Patents
Automatic confirmation method, device, terminal and medium for scheduling operation ticket comprehensive order Download PDFInfo
- Publication number
- CN113806453A CN113806453A CN202111096590.9A CN202111096590A CN113806453A CN 113806453 A CN113806453 A CN 113806453A CN 202111096590 A CN202111096590 A CN 202111096590A CN 113806453 A CN113806453 A CN 113806453A
- Authority
- CN
- China
- Prior art keywords
- order
- model
- comprehensive
- database
- neural network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000012790 confirmation Methods 0.000 title claims abstract description 27
- 238000012549 training Methods 0.000 claims abstract description 34
- 238000013528 artificial neural network Methods 0.000 claims abstract description 14
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 14
- 238000012360 testing method Methods 0.000 claims description 14
- 230000006870 function Effects 0.000 claims description 12
- 230000010354 integration Effects 0.000 claims description 7
- 230000004224 protection Effects 0.000 claims description 6
- 238000013518 transcription Methods 0.000 claims description 6
- 230000035897 transcription Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 4
- 238000011176 pooling Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Strategic Management (AREA)
- Computing Systems (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Educational Administration (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method, a device, a terminal and a medium for automatically confirming a scheduling operation order comprehensive order, wherein the method comprises the following steps: creating an Oracle database, and storing the comprehensive order to the database; constructing an instruction checking model based on a neural network, training the instruction checking model and generating a target model; and checking and confirming the comprehensive order by using the target model. The automatic confirmation method for the dispatching operation order comprehensive order provided by the invention constructs the instruction check model based on the convolutional neural network, effectively improves the confirmation accuracy of the comprehensive order, and simultaneously saves the data transmission time by combining the built database and the instruction check model.
Description
Technical Field
The invention relates to the technical field of electric power operation tickets, in particular to a method, a device, a terminal and a medium for automatically confirming a dispatching operation ticket comprehensive order.
Background
The operation, operation and accident handling of the power grid equipment are important responsibilities for power grid dispatching, and the correct and error-free confirmation of operation tickets is the foundation for preventing misoperation. The power grid dispatching operation order is a strict operation step which is made by ensuring the correct change of the operation mode of the equipment under the requirements of considering the operation mode of the system, protection coordination, safe operation rules and the like.
At present, the release and execution of the comprehensive scheduling order of the power system need final review of the scheduling operator, the network issues the scheduling operator to only execute the release and operation steps of the comprehensive scheduling order, and no decision-making effect is generated on the comprehensive scheduling order.
Disclosure of Invention
The invention aims to provide a method, a device, a terminal and a medium for automatically confirming a scheduling operation ticket comprehensive order, so as to solve the problems of long time consumption and high error rate caused by the dependence on manual examination of the conventional scheduling operation ticket.
In order to overcome the defects in the prior art, the invention provides an automatic confirmation method for a scheduling operation order comprehensive order, which comprises the following steps:
creating an Oracle database, and storing the comprehensive order to the database;
constructing an instruction checking model based on a neural network, training the instruction checking model and generating a target model;
and checking and confirming the comprehensive order by using the target model.
Further, the Oracle database is created by Docker.
Further, the instruction collation model includes:
the convolutional layer is composed of a convolutional neural network and used for extracting a characteristic sequence from input data;
the circulation layer is composed of a circulation neural network and used for predicting the label distribution of the characteristic sequence;
and the transcription layer is composed of a time sequence classification network and is used for carrying out duplication removal and integration operation on the label distribution to obtain an identification result.
Further, training the instruction collation model and generating an object model includes:
stopping training when the output value of the loss function reaches a preset value, and generating a target model; the loss function is:
O＝-ln(Π(x，z)∈Sp(l|x))＝-∑(x，z)∈Slnp(l|x)
wherein, x is a feature sequence obtained by the image through the calculation of the convolutional neural network, z is an OCR character corresponding to the image, s is a data set, p (l | x) is the probability of inputting x and outputting l, and l is the maximum path length.
Further, after the creating an Oracle database and storing the synthesis command in the database, the method further includes:
taking the comprehensive order in the database as a data set;
the data set is as follows 7: and 3, dividing the ratio into a training set and a test set, and respectively training the instruction checking model and testing the instruction checking model.
Further, the content types of the comprehensive order include a bus, a switch, a transformer, a line, a protection and a reclosing.
The invention also provides a device for automatically confirming the comprehensive order of the dispatching operation order, which comprises:
the database creating unit is used for creating an Oracle database and storing the comprehensive order to the database;
the target model generating unit is used for constructing an instruction check model based on a neural network, training the instruction check model and generating a target model;
and the comprehensive order confirming unit is used for checking and confirming the comprehensive order by utilizing the target model.
Further, the instruction collation model includes:
the convolutional layer is composed of a convolutional neural network and used for extracting a characteristic sequence from input data;
the circulation layer is composed of a circulation neural network and used for predicting the label distribution of the characteristic sequence;
and the transcription layer is composed of a time sequence classification network and is used for carrying out duplication removal and integration operation on the label distribution to obtain an identification result.
The present invention also provides a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for automatic confirmation of the scheduling operation ticket integration order as described in any one of the above.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which is executed by a processor to implement the scheduling operation ticket comprehensive order automatic confirmation method as described in any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a method for automatically confirming a scheduling operation order comprehensive order, which comprises the following steps: creating an Oracle database, and storing the comprehensive order to the database; constructing an instruction checking model based on a neural network, training the instruction checking model and generating a target model; and checking and confirming the comprehensive order by using the target model.
The automatic confirmation method for the dispatching operation order comprehensive order provided by the invention constructs the instruction check model based on the convolutional neural network, thereby effectively improving the confirmation accuracy of the comprehensive order; meanwhile, the built database is combined with the instruction checking model, so that the data transmission time is saved.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for automatically confirming a scheduling operation order according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an automatic confirmation apparatus for a scheduling operation ticket comprehensive order according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not used as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
In a first aspect:
referring to fig. 1, an embodiment of the present invention provides a method for automatically confirming a scheduling operation ticket comprehensive order, including:
s10, creating an Oracle database, and storing the comprehensive order to the database;
it should be noted that the comprehensive order is a scheduling instruction issued in the power system scheduling to describe the operation tasks, requirements, and the starting and ending states of the operation objects, and the specific operation steps and operation sequence items are drawn by the order.
In the step, an Oracle database is mainly built through Docker, and the building specific steps are as follows: (1) pulling an oracle mirror image; (2) creating a container; (3) starting the container; (4) and installing an Oracle 11G Client. The Docker is an open-source application container engine, so that developers can package their applications and dependency packages into a portable image, and then distribute the image to any popular Linux or Windows machine, and virtualization can be realized.
S20, constructing an instruction check model based on the neural network, training the instruction check model and generating a target model;
in one embodiment, the instruction collation model includes:
the convolutional layer is composed of a convolutional neural network and used for extracting a characteristic sequence from input data;
the circulation layer is composed of a circulation neural network and used for predicting the label distribution of the characteristic sequence;
and the transcription layer is composed of a time sequence classification network and is used for carrying out duplication removal and integration operation on the label distribution to obtain an identification result.
In one embodiment, the penalty function O that defines the instruction checking model is as follows:
O＝-ln(Π(x，z)∈Sp(l|x))＝-∑(x，z)∈Slnp(l|x)；
wherein, x is a feature sequence obtained by the image through the calculation of the convolutional neural network, z is an OCR character corresponding to the image, s is a data set, p (l | x) is the probability of inputting x and outputting l, and l is the maximum path length.
In one embodiment, the contents of the synthetic order in the database are used as a data set, and the ratio of 7: 3, dividing the data set into a training set and a testing set, inputting the training set into an instruction check model, performing iterative training, and stopping training when a loss function value reaches a preset value to generate a target model; wherein, the preset value generally refers to the lowest loss function value output in all rounds of training.
In one embodiment, the content types of the synthetic command include six types, i.e., bus, switch, transformer, line, protection, and reclosing.
In one embodiment, the specific training steps are as follows:
input: the text block is input and the size is normalized to 32 w, i.e. length is scaled to 32 and width is scaled by the ratio of height and also to the width desired by itself, batch training at the time of training, scaling to [32, Wmax ]), an example being (32,128).
② through two conv and two pooling layers, conv3 layer data size is 256 x 8 x 32, two pooling layer step size is 2, this time output is 256 x 4 x 33.
③ pooling3, the output was 512 x 2 x 34.
Fourthly, at conv7 level, the kernel of the convolution is 22, and the step size stride (1, 1); padding (0, 0);
Wnew＝(2+2*padW-kernel)/strideW+1＝1；
Hnew＝33；
the conv7 layer output is 5121 × 33;
blstm1 layer output 33 x 1256;
blstm2 layer output 33 x 1 x 5530; 5530 the number of characters + non-characters 5529+ 1.
Fifthly, repeating the step (III) and finishing the training when the loss function value reaches the lowest value.
And S30, checking and confirming the comprehensive order by using the target model.
In one embodiment, the trained instruction check model is tested by using a test set, the test precision is set to 0.97, and the test is stopped when the test precision is met. And then, the tested instruction check model is used as a target model to check and confirm the comprehensive instruction.
According to the automatic confirmation method for the dispatching operation order comprehensive order, which is provided by the embodiment of the invention, the order checking model is constructed on the basis of the convolutional neural network, so that the confirmation accuracy rate of the comprehensive order is effectively improved; meanwhile, the built database is combined with the instruction checking model, so that the data transmission time is saved.
In a second aspect:
referring to fig. 2, in an embodiment of the present invention, an automatic confirmation apparatus for a scheduling operation ticket comprehensive order is further provided, including:
the database creating unit 01 is used for creating an Oracle database and storing the comprehensive order into the database;
the target model generation unit 02 is used for constructing an instruction check model based on a neural network, training the instruction check model and generating a target model;
and the comprehensive order confirmation unit 03 is used for checking and confirming the comprehensive order by using the target model.
It should be noted that the automatic confirmation device for scheduling operation ticket comprehensive order is used for executing the method according to the first aspect, specifically:
when step S10 is executed, an Oracle database is built by Docker, and the specific steps of building are as follows: (1) pulling an oracle mirror image; (2) creating a container; (3) starting the container; (4) and installing an Oracle 11G Client. The Docker is an open-source application container engine, so that developers can package their applications and dependency packages into a portable image, and then distribute the image to any popular Linux or Windows machine, and virtualization can be realized.
In executing step S20, the instruction collation model includes:
the convolutional layer is composed of a convolutional neural network and used for extracting a characteristic sequence from input data;
the circulation layer is composed of a circulation neural network and used for predicting the label distribution of the characteristic sequence;
and the transcription layer is composed of a time sequence classification network and is used for carrying out duplication removal and integration operation on the label distribution to obtain an identification result.
Further, a penalty function O defining the instruction check model is as follows:
O＝-ln(Π(x，z)∈Sp(l|x))＝-∑(x，z)∈Slnp(l|x)；
wherein, x is a feature sequence obtained by the image through the calculation of the convolutional neural network, z is an OCR character corresponding to the image, s is a data set, p (l | x) is the probability of inputting x and outputting l, and l is the maximum path length.
Further, taking the content of the synthesis order in the database as a data set, and calculating the ratio of 7: 3, dividing the data set into a training set and a testing set, inputting the training set into an instruction check model, performing iterative training, and stopping training when a loss function value reaches a preset value to generate a target model; wherein, the preset value generally refers to the lowest loss function value output in all rounds of training.
Further, the content types of the comprehensive order include six types, namely a bus, a switch, a transformer, a line, protection and reclosing.
Further, the specific training steps are as follows:
input: the text block is input and the size is normalized to 32 w, i.e. length is scaled to 32 and width is scaled by the ratio of height and also to the width desired by itself, batch training at the time of training, scaling to [32, Wmax ]), an example being (32,128).
② through two conv and two pooling layers, conv3 layer data size is 256 x 8 x 32, two pooling layer step size is 2, this time output is 256 x 4 x 33.
③ pooling3, the output was 512 x 2 x 34.
Fourthly, at conv7 level, the kernel of the convolution is 22, and the step size stride (1, 1); padding (0, 0);
Wnew＝(2+2*padW-kernel)/strideW+1＝1；
Hnew＝33；
the conv7 layer output is 5121 × 33;
blstm1 layer output 33 x 1256;
blstm2 layer output 33 x 1 x 5530; 5530 the number of characters + non-characters 5529+ 1.
Fifthly, repeating the step (III) and finishing the training when the loss function value reaches the lowest value.
In step S30, the trained command verification model is tested using the test set, the test accuracy is set to 0.97, and the test is stopped when the test accuracy is satisfied. And then, the tested instruction check model is used as a target model to check and confirm the comprehensive instruction.
The automatic confirmation device for the dispatching operation order comprehensive order provided by the embodiment of the invention constructs the instruction checking model based on the convolutional neural network, thereby effectively improving the confirmation accuracy of the comprehensive order; meanwhile, the built database is combined with the instruction checking model, so that the data transmission time is saved.
In a third aspect:
in an embodiment, there is further provided a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the scheduled operation ticket integrated order automatic confirmation method as described above.
The processor is used for controlling the whole operation of the terminal equipment so as to complete all or part of the steps of the dispatching operation order comprehensive order automatic confirmation method. The memory is used to store various types of data to support operation at the terminal device, and these data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The terminal Device may be implemented by one or more Application Specific1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the method for automatic determination of the integrated command of the scheduling operation ticket according to any one of the embodiments described above, so AS to achieve the technical effects consistent with the above methods.
In an embodiment, there is also provided a computer-readable storage medium including program instructions, which when executed by a processor, implement the steps of the scheduling operation ticket comprehensive order automatic confirmation method according to any one of the above embodiments. For example, the computer-readable storage medium may be the above-mentioned memory including program instructions that are executable by the processor of the terminal device to perform the method for automatically confirming a scheduling operation ticket comprehensive order according to any one of the above-mentioned embodiments, and achieve the technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for automatically confirming a scheduling operation order comprehensive order is characterized by comprising the following steps:
creating an Oracle database, and storing the comprehensive order to the database;
constructing an instruction checking model based on a neural network, training the instruction checking model and generating a target model;
and checking and confirming the comprehensive order by using the target model.
2. The automatic confirmation method of the dispatching operation ticket integrated order of claim 1, wherein the Oracle database is created by Docker.
3. The method of claim 1, wherein the order collation model includes:
the convolutional layer is composed of a convolutional neural network and used for extracting a characteristic sequence from input data;
the circulation layer is composed of a circulation neural network and used for predicting the label distribution of the characteristic sequence;
and the transcription layer is composed of a time sequence classification network and is used for carrying out duplication removal and integration operation on the label distribution to obtain an identification result.
4. The method of claim 3, wherein the training the order collation model and generating the target model comprises:
stopping training when the output value of the loss function reaches a preset value, and generating a target model; the loss function is:
O＝-ln(Π(x，z)∈Sp(l|x))＝-∑(x，z)∈Slnp(l|x)
wherein, x is a feature sequence obtained by the image through the calculation of the convolutional neural network, z is an OCR character corresponding to the image, s is a data set, p (l | x) is the probability of inputting x and outputting l, and l is the maximum path length.
5. The automatic confirmation method of the dispatching operation order comprehensive order according to any one of claims 1 to 4, wherein after the creating an Oracle database and storing the comprehensive order in the database, the method further comprises:
taking the comprehensive order in the database as a data set;
the data set is as follows 7: and 3, dividing the ratio into a training set and a test set, and respectively training the instruction checking model and testing the instruction checking model.
6. The method for automatically confirming the dispatching operation order comprehensive order according to any one of claims 1 to 4, wherein the content types of the comprehensive order comprise buses, switches, transformers, lines, protections and reclosures.
7. An automatic confirmation apparatus for a scheduling operation ticket integrated order, comprising:
the database creating unit is used for creating an Oracle database and storing the comprehensive order to the database;
the target model generating unit is used for constructing an instruction check model based on a neural network, training the instruction check model and generating a target model;
and the comprehensive order confirming unit is used for checking and confirming the comprehensive order by utilizing the target model.
8. The automatic confirmation apparatus of scheduling operation ticket integrated order of claim 7, wherein the order collation model includes:
the convolutional layer is composed of a convolutional neural network and used for extracting a characteristic sequence from input data;
the circulation layer is composed of a circulation neural network and used for predicting the label distribution of the characteristic sequence;
and the transcription layer is composed of a time sequence classification network and is used for carrying out duplication removal and integration operation on the label distribution to obtain an identification result.
9. A terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the scheduled operation ticket integrated order automatic confirmation method of any one of claims 1 to 6.
10. A computer-readable storage medium having stored thereon a computer program, wherein the computer program is executed by a processor to implement the scheduling operation ticket integrated order automatic confirmation method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111096590.9A CN113806453A (en) | 2021-09-18 | 2021-09-18 | Automatic confirmation method, device, terminal and medium for scheduling operation ticket comprehensive order |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111096590.9A CN113806453A (en) | 2021-09-18 | 2021-09-18 | Automatic confirmation method, device, terminal and medium for scheduling operation ticket comprehensive order |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113806453A true CN113806453A (en) | 2021-12-17 |
Family
ID=78895900
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111096590.9A Pending CN113806453A (en) | 2021-09-18 | 2021-09-18 | Automatic confirmation method, device, terminal and medium for scheduling operation ticket comprehensive order |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113806453A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109271374A (en) * | 2018-10-19 | 2019-01-25 | 国网江苏省电力有限公司信息通信分公司 | A kind of database health scoring method and scoring system based on machine learning |
| CN110175324A (en) * | 2019-04-11 | 2019-08-27 | 贵州电网有限责任公司 | A kind of operation of power networks operational order method of calibration and system based on data mining |
| CN110852324A (en) * | 2019-08-23 | 2020-02-28 | 上海撬动网络科技有限公司 | Deep neural network-based container number detection method |
| CN111930601A (en) * | 2020-05-14 | 2020-11-13 | 国家电网有限公司客户服务中心 | Deep learning-based database state comprehensive scoring method and system |
| CN112860872A (en) * | 2021-03-17 | 2021-05-28 | 广东电网有限责任公司 | Self-learning-based method and system for verifying semantic compliance of power distribution network operation tickets |
| CN113140219A (en) * | 2021-05-08 | 2021-07-20 | 广东电网有限责任公司 | Regulation and control instruction generation method and device, electronic equipment and storage medium |
-
2021
- 2021-09-18 CN CN202111096590.9A patent/CN113806453A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109271374A (en) * | 2018-10-19 | 2019-01-25 | 国网江苏省电力有限公司信息通信分公司 | A kind of database health scoring method and scoring system based on machine learning |
| CN110175324A (en) * | 2019-04-11 | 2019-08-27 | 贵州电网有限责任公司 | A kind of operation of power networks operational order method of calibration and system based on data mining |
| CN110852324A (en) * | 2019-08-23 | 2020-02-28 | 上海撬动网络科技有限公司 | Deep neural network-based container number detection method |
| CN111930601A (en) * | 2020-05-14 | 2020-11-13 | 国家电网有限公司客户服务中心 | Deep learning-based database state comprehensive scoring method and system |
| CN112860872A (en) * | 2021-03-17 | 2021-05-28 | 广东电网有限责任公司 | Self-learning-based method and system for verifying semantic compliance of power distribution network operation tickets |
| CN113140219A (en) * | 2021-05-08 | 2021-07-20 | 广东电网有限责任公司 | Regulation and control instruction generation method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dillon et al. | Generating oracles from your favorite temporal logic specifications | |
| CN109635110A (en) | Data processing method, device, equipment and computer readable storage medium | |
| CN113095050A (en) | Intelligent ticketing method, system, equipment and storage medium | |
| CN113435998A (en) | Loan overdue prediction method and device, electronic equipment and storage medium | |
| CN112732565B (en) | Evaluation method for continuous integration of software, computer equipment and medium | |
| CN111680801A (en) | Operation checking method, device, equipment and storage medium | |
| CN112926296A (en) | Data verification method and device, electronic equipment and storage medium | |
| US9032346B2 (en) | Method and apparatus for creating and managing waiver descriptions for design verification | |
| CN109242516A (en) | The single method and apparatus of processing service | |
| CN109165286A (en) | Automatic question-answering method, device and computer readable storage medium | |
| CN116227584A (en) | Pre-training language model building method, system and medium for power industry | |
| CN113806453A (en) | Automatic confirmation method, device, terminal and medium for scheduling operation ticket comprehensive order | |
| CN109034520A (en) | Evaluation of employee method and terminal device | |
| CN112232078A (en) | Scheduling operation ticket auditing method based on bidirectional GRU and attention mechanism | |
| CN112114935A (en) | Hot plug method and related equipment | |
| CN113807975B (en) | Method, device, computer equipment and storage medium for checking information | |
| CN110750637A (en) | Text abstract extraction method and device, computer equipment and storage medium | |
| CN115328772A (en) | Learning method for excitation combination and module correlation and test script generation method | |
| CN113314108B (en) | Method, apparatus, device, storage medium and program product for processing voice data | |
| CN114547272A (en) | Training method and device of dialogue generation model, and dialogue generation method and device | |
| CN113935328A (en) | Text abstract generation method and device, electronic equipment and storage medium | |
| CN114519650A (en) | Intelligent ticket checking method, equipment and storage medium for dispatching operation tickets of urban power grid | |
| CN113272813B (en) | Custom data stream hardware simulation method, device, equipment and storage medium | |
| CN112685546A (en) | Man-machine multi-turn dialogue method and device and computer readable storage medium | |
| CN109829814B (en) | Method, device, equipment and storage medium for generating historical repayment information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |